Current PhD Students

Xiaotong Li - Gerstein lab & Pusztai lab (2014) | ORCID: 0000-0003-1644-6835
Xiaotong Li's picture

Xiaotong is interested in next-generation sequencing data analysis, with a major focus on breast cancer. Currently she is working on whole genome sequencing analysis on inflammatory breast cancer, and heterogeneity analysis.

 
Daniel Chawla - Kleinstein lab (2015) | ORCID: 0000-0001-7667-9337
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Kevin Lopez - Brandt lab (2015) | ORCID: 0000-0002-2901-5674
Kevin Lopez's picture

Kevin works on multi model learning using deep learning and convolutional neural networks. He is also working on expanding the capabilities of Yale Image Finder by applying deep learning methods to classify images from PubMed publications.

David Chang - Brandt lab & Zhao lab (2016) | ORCID: 0000-0002-2065-0778
David Chang's picture

David is studying recent advances in deep learning and NLP to enable effective integration of multiple EHR data modalities to improve information extraction

Hussein Mohsen - Gerstein lab (2016) | ORCID: 0000-0002-6263-8865
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Hussein’s main research interests are machine learning and cancer genomics. He is working on projects that leverage genomics big data to explore variation patterns in cancer. In particular, he is interested in developing machine and deep learning methods that study the underlying interaction between somatic and germline genetic variations in pan-cancer tumor types.

Nir Neumark - Kaminski lab & Coifman lab (2016) | ORCID: 0000-0002-5560-6136
Jiawei Wang - Zhao lab (2016) | ORCID: 0000-0003-2627-4897

Jiawei’s research interest lies in imaging genetics and mental diseases. He is working on gene expression analysis to help discover the etiology of PTSD and graphical models to study brain functional and structural network. 

Zhaolong Yu - Zhao lab (2016) | ORCID: 0000-0001-9585-2465
Zhaolong Yu's picture

Zhaolong’s current research focus is patient outcomes prediction based on multi-omics data. He is particularly interested in developing machine learning algorithms to better predict cancer patient outcomes.

Evan Cudone - McDougal lab (2017) | ORCID: 0000-0002-1055-1645
Evan Cudone's picture

Evan utilizes natural language processing and machine learning to facilitate computational neuroscience research. Using unsupervised topic modeling and deep learning language models he characterizes neuroscience simulation research from its academic literature and source code.

J. Nick Fisk - Townsend Lab (2017) | ORCID: 0000-0002-1940-393X
J. Nick Fisk's picture

Nick works on constructing phylogenetic trees to answer questions about cancer development, evolution, and the selective pressure treatment induces on the system. He also works on developing and implementing methods to optimize general phylogenetic experimental design.

Jiahao Gao - Gerstein lab (2017) | ORCID: 0000-0002-6311-3526

Jiahao focuses on the analysis of high throughput sequencing data, with special interest in ChIP-seq and other functional genomics technologies. He is currently working on denoising the ChIP-seq binding sites in order to improve the de novo inference of transcription factor binding motifs.

Scott Gigante - Krishnaswamy lab (2017) | ORCID: 0000-0002-4544-2764
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Scott’s research is focused the development of methods in deep learning and graph signal processing to understand the structure of single-cell genomics data, especially single-cell RNA sequencing data. He is particularly interested in how to make the most of many high-resolution noisy measurements, and how manifold learning techniques can facilitate this understanding.

Pranav Kantroo - Wagner lab & Machta lab (2017)
Tianxiao Li - Gerstein lab (2017) | ORCID: 0000-0002-9147-7511

Tianxiao is interested in applying novel machine learning techniques to inference of gene regulatory networks and 3D genomics. He is now working on developing novel methods to associate 3D genomics structures with gene regulatory elements.

Wei Liu - Zhao lab (2017) | ORCID: 0000-0003-2558-1377
Wei Liu's picture

Wei’s research is mainly focused on understanding genetic architecture and mechanisms of complex diseases. She is now working in developing statistical methods investigating disease-associated genes using multi-omics data including genetics, epigenetics and transcriptomics data. Wei is also interested in population genetics and causal inference.

Rihao Qu - Kluger lab & Flavell lab (2017) | ORCID: 0000-0002-8258-8287

Rihao’s research interests include high-throughput sequencing analysis and immunogenomics. He is working on developing computational and statistical methods to process high-dimensional single cell sequencing data and help understand the genetic mechanisms underlying immune pathways.

Yixuan Ye - Zhao lab (2017) | ORCID: 0000-0002-2643-665X
Yixuan Ye's picture

Yixuan’s research focuses on the genetic risk prediction for chronic diseases and cancer. She is currently working on exploring the interaction between gene, lifestyle and diseases. She is also interested in developing new methods to improve genetic prediction accuracy and the interpretation of polygenic risk score. 

Geyu Zhou - Zhao lab (2017) | ORCID: 0000-0002-4049-0193

Geyu’s research interest is statistical genomics and genetics. He is experienced in gene expression data analysis. He is currently working on developing statistical methods to compute polygenic risk score.

Edel Aron - Kleinstein lab (2018) | ORCID: 0000-0002-8683-4772
Edel Aron's picture

Edel’s research is focused on developing novel computational methods capable of inferring networks of interactions present in the skin lesions of patients with Lyme disease. Her goal is to better understand the nature of the lesions and the full immunological response to the disease as well as to be able to predict its clinical course.

Egbert Castro - Krishnaswamy lab (2018) | ORCID: 0000-0001-7883-5241
Egbert Castro's picture

Egbert is interested in developing improved methods for biomolecule representation, particularly with the goal of better connecting structure-property relationships. In this pursuit, his work combines methods from machine learning as well as graph signal processing to build more predictive and interpretable representations of entities such as small molecules or proteins.

Guannan Gong - Krumholz lab (2018) | ORCID: 0000-0002-4972-6705
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Guannan’s research is to explore different digital phenotyping approaches based on electronic healthcare data, and to identify the state of the art pipeline for detection of adverse events, such as oncology patients developing bleeds post treatment, and identification of patients who are eligible for clinical trials based on inclusion and exclusion criteria.

Alex Grigas - O'Hern lab (2018) | ORCID: 0000-0002-1588-2996
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Alex is interested in the statistical mechanics of protein structure and his current research project is focused on protein decoy detection, which involves developing scoring metrics to distinguish real protein structures from erroneous ones generated using computational protein design software.

Jeff Mandell - Townsend lab (2018) | ORCID: 0000-0002-3839-2543
Jeff Mandell's picture

Jeff is currently studying the somatic evolution of cancer with the goal of guiding treatment plans and drug development. More broadly, Jeff is interested in developing tools and methods to organize large amounts of heterogeneous genomics data into coherent biological knowledge.

Kyra Thrush - M. Levine lab (2018) | ORCID: 0000-0002-3991-9597

Kyra’s research revolves around the epigenetic patterning that exists as a result of the aging process. In particular, she is developing biomarkers to predict incidence and severity of cognitive impacts in Alzheimer’s dementia, a disease highly associated with human aging.

Ana Berthel - Gerstein lab (2019) | ORCID: 0000-0001-6849-982X
Jeremy Gygi - Kleinstein lab (2019) | ORCID: 0000-0001-8567-7472
Diana Leung - M. Levine lab & Kluger lab (2019) | ORCID: 0000-0002-5999-9547
Wes Lewis - Kluger lab (2019) | ORCID: 0000-0002-1192-8862
Yaroslav Markov - M. Levine lab (2019) | ORCID: 0000-0001-8778-4909
Eric Ni - Gerstein lab (2019) | ORCID: 0000-0002-4530-0707
Vimig Socrates - Brandt lab (2019) | ORCID: 0000-0001-7955-9875
Andrea Tamminga - Cotsapas lab (2019) | ORCID: 0000-0001-9049-0928
Aarthi Venkat - Krishnaswamy lab (2019) | ORCID: 0000-0003-0298-0172
Aarthi Venkat's picture

Aarthi is interested in developing improved methods to study cancer immunogenomics. To this end, her research focuses on machine learning and graph-based methods that leverage the rich properties of single-cell RNA sequencing data.

Mamie Wang - Kleinstein lab & Kluger lab (2019) | ORCID: 0000-0002-3453-7805
Junchen Yang - Kluger lab (2019) | ORCID: 0000-0003-0988-1564

Junchen’s major interest is developing and applying computational methods to analyze high-dimensional transcriptomic data. He is also interested in tackling emerging sequencing protocol-oriented questions.  Currently, he is working on cell-interaction interpretations from single-cell RNA-sequencing data, and feature selection problems using novel deep learning methods.

Seyedeh Maryam Zekavat - Zhao lab (2019) | ORCID: 0000-0003-4026-8944
Maryam is a MD-PhD student interested in combining germline and somatic genomics with deep phenotyping to discover and understand the causal factors of disease.
Biqing Zhu - Zhao lab (2019) | ORCID: 0000-0002-7428-6297
Biqing Zhu's picture

Biqing’s research focuses on single cell data modeling and analysis to better understand its underlying structure and find biologically meaningful signals. And she is currently working on developing statistical methods to conduct CyTOF data clustering through multi-omics data deconvolution. 

Sarah Dudgeon - (2020)
Noah Lee - (2020)
Jason Liu - (2020)
A. Ram - (2020)